Response-reinforcement learning is dependent on N-methyl-d-aspartate receptor activation in the nucleus accumbens core

The nucleus accumbens, a site within the ventral striatum, is best known for its prominent role in mediating the reinforcing effects of drugs of abuse such as cocaine, alcohol, and nicotine. Indeed, it is generally believed that this structure subserves motivated behaviors, such as feeding, drinking, sexual behavior, and exploratory locomotion, which are elicited by natural rewards or incentive stimuli. A basic rule of positive reinforcement is that motor responses will increase in magnitude and vigor if followed by a rewarding event. It is likely, therefore, that the nucleus accumbens may serve as a substrate for reinforcement learning. However, there is surprisingly little information concerning the neural mechanisms by which appetitive responses are learned. In the present study, we report that treatment of the nucleus accumbens core with the selective competitive N-methyl-d-aspartate (NMDA) antagonist 2-amino-5-phosphonopentanoic acid (AP-5; 5 nmol/0.5 μl bilaterally) impairs response-reinforcement learning in the acquisition of a simple lever-press task to obtain food. Once the rats learned the task, AP-5 had no effect, demonstrating the requirement of NMDA receptor-dependent plasticity in the early stages of learning. Infusion of AP-5 into the accumbens shell produced a much smaller impairment of learning. Additional experiments showed that AP-5 core-treated rats had normal feeding and locomotor responses and were capable of acquiring stimulus-reward associations. We hypothesize that stimulation of NMDA receptors within the accumbens core is a key process through which motor responses become established in response to reinforcing stimuli. Further, this mechanism, may also play a critical role in the motivational and addictive properties of drugs of abuse.

Speech technology in the year 2001.

This paper introduces the session "Technology in the Year 2001" and is the first of four papers dealing with the future of human-machine communication by voice. In looking to the future it is important to recognize both the difficulties of technological forecasting and the frailties of the technology as it exists today--frailties that are manifestations of our limited scientific understanding of human cognition. The technology to realize truly advanced applications does not yet exist and cannot be supported by our presently incomplete science of speech. To achieve this long-term goal, the authors advocate a fundamental research program using a cybernetic approach substantially different from more conventional synthetic approaches. In a cybernetic approach, feedback control systems will allow a machine to adapt to a linguistically rich environment using reinforcement learning.